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What is already known about this subject • Many studies have investigated the effects of thiazolidinediones on isolated biochemical markers (biomarkers) or sets of markers in Type 2 diabetes mellitus (T2DM) patients and healthy volunteers. • However, a limited number of parameters is not capable of capturing the broad response to pharmacological intervention with these types of (pleiotropic) drugs, which are known to activate the nuclear transcription factor peroxisome proliferator activated receptor gamma (PPARγ). • Our study tested the new hypothesis (primary objective) that nuclear magnetic resonance (NMR)‐based metabolomics, capable of providing a readout of global metabolite concentrations in biofluids, could provide a better (more holistic) picture of the the multiparametric response to pharmacological intervention with a PPARγ agonist and thus yield a broad array of biomarkers (‘fingerprint’) that could be used to support and expedite clinical development of novel thiazolidinediones. What this study adds • NMR‐based metabolomics coupled with sophisticated bioinformatics is indeed capable of identifying rapid changes in global metabolite profiles in urine and plasma (treatment ‘fingerprints’), which may be linked to the well‐documented early changes in hepatic insulin senstitivity following thiazolidinedione intervention in T2DM patients. • Consequently, this approach (upon proper validation) comprises an important new addition to the early clinical development ‘proof of concept’ toolbox for thiazolidinediones, and may also be applicable to other classes of drugs. Aims To explore the usefulness of metabolomics as a method to obtain a broad array of biomarkers for the pharmacological effects of rosiglitazone (RSG) in plasma and urine samples from patients with type 2 diabetes mellitus (T2DM) and healthy volunteers (HVs). Additionally, we explored the differences in metabolite concentrations between T2DM patients and HVs to identify a putative metabolic disease fingerprint for T2DM. Methods 1H nuclear magnetic resonance (NMR) spectroscopy was used to profile blood plasma and urine samples of 16 T2DM patients and 16 HVs receiving RSG 4 mg or placebo twice daily for 6 weeks. Multivariate analyses were employed to identify treatment‐ and disease‐related effects on global endogenous metabolite profiles. Results RSG treatment led to a rapid relative reduction in urinary hippurate and aromatic amino acids as well as an increase in plasma branched chain amino acids and alanine, glutamine and glutamate in the T2DM group. No RSG treatment effects were noted in the HV group. Exploratory baseline analyses showed that urine and plasma metabolites discriminated between genders and disease state. T2DM patients showed a relative increase in urinary concentrations of several amino acids, citrate, phospho(enol)pyruvate and hippurate. Putative T2DM‐related changes in plasma were largely attributable to increased plasma lipids. Conclusion The results of this study indicate that NMR‐based metabolomics of urine and blood pla...

BackgroundBeing able to visualize multivariate biological treatment effects can be insightful. However the axes in visualizations are often solely defined by variation and thus have no biological meaning. This makes the effects of treatment difficult to interpret.MethodsA statistical visualization method is presented, which analyses and visualizes the effects of treatment in individual subjects. The visualization is based on predefined biological processes as determined by systems-biological datasets (metabolomics proteomics and transcriptomics). This allows one to evaluate biological effects depending on shifts of either groups or subjects in the space predefined by the axes, which illustrate specific biological processes. We built validated multivariate models for each axis to represent several biological processes. In this space each subject has his or her own score on each axis/process, indicating to which extent the treatment affects the related process.ResultsThe health space model was applied to visualize the effects of a nutritional intervention, with the goal of applying diet to improve health. The model was therefore named the 'health space' model. The 36 study subjects received a 5-week dietary intervention containing several anti-inflammatory ingredients. Plasma concentrations of 79 proteins and 145 metabolites were quantified prior to and after treatment. The principal processes modulated by the intervention were oxidative stress, inflammation, and metabolism. These processes formed the axes of the 'health space'. The approach distinguished the treated and untreated groups, as well as two different response subgroups. One subgroup reacted mainly by modulating its metabolic stress profile, while a second subgroup showed a specific inflammatory and oxidative response to treatment.ConclusionsThe 'health space' model allows visualization of multiple results and to interpret them. The model presents treatment group effects, subgroups and individual responses.

Integrative (or systems biology) is a new approach to analyzing biological entities as integrated systems of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. Here, we demonstrate the application of intregrative biological analysis to a mammalian disease model, the apolipoprotein E3-Leiden (APO*E3) transgenic mouse. Mice selected for the study were fed a normal chow diet and sacrificed at 9 weeks of age-conditions under which they develop only mild type I and II atherosclerotic lesions. Hepatic mRNA expression analysis showed a 25% decrease in APO A1 and a 43% increase in liver fatty acid binding protein expression between transgenic and wild type control mice, while there was no change in PPAR-alpha expression. On-line high performance liquid chromatography-mass spectrometry quantitative profiling of tryptic digests of soluble liver proteins and liver lipids, coupled with principle component analysis, enabled rapid identification of early protein and metabolite markers of disease pathology. These included a 44% increase in L-FABP in transgenic animals compared to controls, as well as an increase in triglycerides and select bioactive lysophosphatidylcholine species. A correlation analysis of identified genes, proteins, and lipids was used to construct an interaction network. Taken together, these results indicate that integrative biology is a powerful tool for rapid identification of early markers and key components of pathophysiologic processes, and constitute the first application of this approach to a mammalian system.

Osteoarthritis (OA), one of the most common diseases among the elderly, is characterized by the progressive destruction of joint tissues. Its etiology is largely unclear and no effective disease-modifying treatment is currently available. Metabolic fingerprinting provides a novel tool for the identification of biomarkers. A metabolic fingerprint consists of a typical combination of metabolites in a biological fluid and is identified by a combination of (1)H NMR spectroscopy and multivariate data analysis (MVDA). The current feasibility study was aimed at identifying a metabolic fingerprint for OA and applying this in a nutritional intervention study. Urine samples were collected from osteoarthritic male Hartley guinea pigs (n = 44) at 10 and 12 mo of age, treated from 4 mo onward with variable vitamin C doses (2.5-3, 30 and 150 mg/d) and from healthy male Strain 13 guinea pigs (n = 8) at 12 mo of age, treated with 30 mg vitamin C/d. NMR measurements were performed on all urine samples. Subsequently, MVDA was carried out on the data obtained using NMR. An NMR fingerprint was identified that reflected the osteoarthritic changes in guinea pigs. The metabolites that comprised the fingerprint indicate that energy and purine metabolism are of major importance in OA. Metabolic fingerprinting also allowed detection of differences in OA-specific metabolites induced by different dietary vitamin C intakes. This study demonstrates the feasibility of metabolic fingerprinting to identify disease-specific profiles of urinary metabolites. NMR fingerprinting is a promising means of identifying new disease markers and of gaining fresh insights into the pathophysiology of disease.